
During the development of machine learning tasks, label distribution learning (LDL) [1]} is an important machine learning paradigm that leverages a function to map a single instance to a set of labels (labels are represented in the form of descriptive degrees and the sum of the descriptive degrees is 1).
Unlike the multi-label learning paradigm, the LDL conveys a richer semantic content in terms of characterizing the instance's emotions [2]}, [3]} and estimating the learning task's uncertainty [4]}, [5]}, [6]}.
